• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.215-227
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• 2022

In the digital transformation paradigm, IT employee work as a key human resource to accept new technologies and to lead their organization to be settled them efficiently. However, due to relatively short term of their job life and high turnover rate, the companies and the organizations are still experiencing problem the lack of IT manpower or turnover. In this study, it attempted to analyze the relationship between IT employee's technostress factors such as techno-overload, techno-complexity, techno-uncertainty, techno-invasion, and techno-insecurity and job burnout through stress coping. To reveal the structural relationship between main variables, the survey was conducted on 318 IT employees. An EFA, CFA, and reliability analysis were performed to confirm reliability and validity, and the structural equation model was conducted to testify research hypotheses. The main results are as follows. First, it was found that techno-uncertainty and techno-insecurity had the significant positive effect on problem focused coping(PFC). And, techno-complexity, techno-uncertainty, and techno-insecurity were found to have a significant positive effect on emotion focused coping(EFC). Second, in the relationship between stress coping and job burnout, it was found that EFC had a significant positive effect on burnout. Third, in the relationship between technostress and burnout, techno-uncertainty and techno-invasion were found to have a significant positive effect on burnout. In addition, it was found that the mediator effect of stress coping between techno-overload and techno-complexity through EFC. Therefore, these outputs are expected to suggest how to motivate IT employees who work as key role in efficient management on IT assets and strengthen competitiveness in digital transformation paradigm.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.153-159
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• 2024

This study is a descriptive research study to determine the relationship between middle-aged women's social support, resilience and quality of life, and to identify factors that affect quality of life. The participants were 162 middle-aged women in G City. Data was collected from May 10 to June 10, 2023. Data were analyzed through descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient, and multiple regression using SPSS 25.0 program. The social support was 3.36±0.38, resilience was 3.39±0.42, and quality of life was 3.18±0.50. Quality of life was positively correlated with social support(r=.502, p<.001), resilience(r=.530, p<.001). Factors that have a significant impact on quality of life include resilience(β=.422, p<.001), social support(β=.412, p<.001) and health status(β=.212, p=.001). The total explanatory power of these variables on quality of life was 39.2%. Therefore, it is necessary to develop effective programs and strategies to improve the quality of life of middle-aged women by improving their resilience, social support, and health status.

• Information Systems Review
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• v.23 no.4
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• pp.183-198
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• 2021

Due to the Covid-19 pandemic, the home-training app market is growing rapidly and numerous apps are entering the market. It is becoming more difficult for an app to secure the profitability. In this study, by analyzing actual user data of a reward-based home-training app, we propose a model that predicts cash-cow users of the app. Cash-cow users are the users who watch in-stream ads to watch training videos although they cannot earn any rewards by doing so. Thus, these users make profits for the app yet do not incur any costs. The results of this study show that the users who irregularly watch training videos are more likely to be cash-cow users than the users who regularly watch training videos. This result suggests that, paradoxically, for sustainable profitability, home-training apps may need to find a way to retain the users who watch training videos irregularly so that they can be satisfied with the service and continue use the apps.

• Information Systems Review
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• v.23 no.3
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• pp.201-223
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• 2021

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

• Information Systems Review
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• v.23 no.3
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• pp.177-200
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• 2021

Recently, the use of prepaid electronic payments such as electronic wallets, digital currency and prepaid points is gradually increasing. Prepaid electronic payments has the characteristic of being used after charging first. This study empirically investigated the factors affecting the intention to use online charging in order to help improve the service that require prepaid recharge by applying transformed TAM. Since there are not many previous studies for the intention to use online charging, we extract factors through preceding researches for electronic cash and mobile easy payment. Also we analyze the intention to use online charging for transportation card users, focusing on the moderating effects. As a result of the study, it was found that 'convenience', 'ubiquity', and 'self-efficacy' among the independent variables had a positive (+) effect on mediation variable 'perceived usefulness'. 'Perceived usefulness' was analyzed to have a significant influence on the dependent variable 'usage intention'. According to users' gender, internet usage time, internet shopping frequency, online charging frequency and transportation card usage type, the moderating effect was significant on 'perceived usefulness' and 'usage intention'. As an implication, it was suggested that service improvement and differentiated marketing are needed in direction of increasing the usefulness of services. Additional research directions were proposed for services such as e-wallets, prepaid points and digital currencies by adding other factors and moderate variables.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Information Systems Review
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• v.23 no.3
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• pp.25-50
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• 2021

Though there have been a lot of studies about online product review, the effects of highlighted reviewhave not been examined enough. Highlighted review is a type of review that the platform designer changes its size or position in order to highlight without any sponsorship or incentive. The main subject of this study is about how highlighted review type affects consumer's perception and behavior in online information acquisition. We collected data from 171 subjects to test hypotheses. Using three different types of screen captures, we compared three groups - general review group, positive highlighted review only group, and both positive and negative highlighted review group. As a result, disclosing both of positiveand negative highlighted review was perceived more useful than disclosing only positive highlighted review. However, correlation between highlighted review type and review skepticism was not statistically significant. The impacts of review usefulness and skepticism on platform credibility were statistically significant, and the correlation between platform credibility and usage intention was also significant. All of results is almost similar across two product types, search goods and experiential goods. This research provides practical implications to online shopping platform designers when they design review systems to make people use their platforms.

• Korean Journal of Heritage: History & Science
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• v.57 no.2
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• pp.28-50
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• 2024

A fact-finding survey of the Demilitarized Zone can be said to be a very meaningful academic survey linked to previous index surveys of protected military areas and municipal and excavation surveys of ruins and military sites on Mount Dora. Not a few ruins were first discovered in this survey, and the locations, structures, and restoration artifacts of the previously investigated ruins were confirmed differently, raising the need for a detailed investigation. In particular, it is noteworthy that various relics from the Paleolithic Age to the Joseon Dynasty were recovered from relics dispersion sites such as Josan-ri and Cheorwon Gangseo-ri in Paju, and Hoengsan-ri Temple Site is also a Buddhist relic in the Demilitarized Zone. However, in the case of some graveyards and relics sites in the Paju region, it was an opportunity to understand the reality that they are not safe from cultivation and development, and the ruins of Cheorwon Capital Castle, Seongsanseong Fortress, Jorangjin Bastion, and Gangseo-ri Bastion were damaged during the construction of military facilities, and an urgent investigation is needed. Also, farmland and hilly areas around the ruins of Jangdan, Gunnae-myeon, and Gangsan-ri have not been properly investigated for buried cultural assets due to small-scale development. Therefore, it is an important time for the relevant authorities and agencies to cooperate more closely to establish special management and medium- to long-term investigation measures for the cultural heritage in the Demilitarized Zone based on the results of this fact-finding investigation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.43-52
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• 2024

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.